Abstract
Clustering is a fundamental unsupervised representation learning task with wide application in computer vision and pattern recognition. Deep clustering utilizes deep neural networks to learn latent representation, which is suitable for clustering. However, previous deep clustering methods, especially image clustering, focus on the features of the data itself and ignore the relationship between the data, which is crucial for clustering. In this paper, we propose a novel Deep Structure and Attention aware Subspace Clustering (DSASC), which simultaneously considers data content and structure information. We use a vision transformer to extract features, and the extracted features are divided into two parts, structure features, and content features. The two features are used to learn a more efficient subspace structure for spectral clustering. Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Our code will be available at https://github.com/cs-whh/DSASC.
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Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions. This paper is supported by the National Natural Science Foundation of China (Grant Nos. 61972264, 62072312), Natural Science Foundation of Guangdong Province (Grant No. 2019A1515010894) and Natural Science Foundation of Shenzhen (Grant No. 20200807165235002).
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Wu, W., Wang, W., Kong, S. (2024). Deep Structure and Attention Aware Subspace Clustering. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_12
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DOI: https://doi.org/10.1007/978-981-99-8462-6_12
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